Subsetting data and graphing on a world map using sf in R: "Error: Object not found"
Subsetting data and graphing on a world map using sf in R: "Error: Object not found"
我正在使用 R 中 spData 包中的 world 和 worldbank_df 数据集，我需要对我的数据进行子集化并绘制结果图表。我正在选择“大陆”和“urban_pop”列，删除所有 NA 值，按大陆分组，并汇总所有大陆的平均城市人口。但是，当我使用 geom_sf 调用绘制结果图表时，出现错误：
Error in FUN(X[[i]], ...) : object 'mean_urban_pop' not found
我需要在世界地图中绘制此数据，但它对我不起作用，因为 geom 坐标未传输到我的新数据集。
如何将这些结果绘制成图表？
注意：下面投影的坐标系是我必须使用的。
这是我的代码：
library(tidyverse)
library(sf)
library(spData)
library(ggplot2)
#Load in the data
world <- spData::world
wdb <- spData::worldbank_df
#Combine the data frames
wld_jn <- left_join(world, wdb, by = c('iso_a2', 'name_long' = 'name'))
#Reproject to required coordinate system. Obtained from: https://spatialreference.org/ref/sr-org/6/
wld_jn <- st_transform(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
def_wld_jn <- st_set_crs(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
#Graph mean urban population across continent:
wld_jn %>%
select(continent, urban_pop) %>%
drop_na() %>%
group_by(continent) %>%
summarise_at(vars(urban_pop), list(mean_urban_pop = mean)) %>%
ggplot(.) +
geom_sf(data = wld_jn, aes(fill = mean_urban_pop)) +
scale_fill_gradient2(midpoint = 285)+
guides(fill = guide_colorbar(title = "Population",
title.position = "bottom",
title.theme = element_text(size = 10,
face = "bold",
colour = "gray70",
angle = 0))) +
ggtitle("World Urban Population") +
theme(plot.title = element_text(hjust = 0.5))
看起来问题出在使用 summarise() 而不是 mutate() 上；当您使用 summarise() 时，您只保留感兴趣的变量，例如
library(tidyverse)
head(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
mtcars %>%
group_by(cyl) %>%
summarise(mean_mpg = mean(mpg)) %>%
head()
#> # A tibble: 3 × 2
#> cyl mean_mpg
#> <dbl> <dbl>
#> 1 4 26.7
#> 2 6 19.7
#> 3 8 15.1
mtcars %>%
group_by(cyl) %>%
mutate(mean_mpg = mean(mpg)) %>%
head()
#> # A tibble: 6 × 12
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb mean_mpg
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 19.7
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 19.7
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 26.7
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 19.7
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 15.1
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 19.7
由 reprex package (v2.0.1)
于 2022-02-23 创建
如果您将 sumamrise() 更改为 mutate()（并将 geometry = geom 添加到 geom_sf()），则不会出现错误。这能解决您的问题吗？
library(tidyverse)
library(sf)
#> Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
#install.packages("spData")
library(spData)
#> To access larger datasets in this package, install the spDataLarge
#> package with: `install.packages('spDataLarge',
#> repos='https://nowosad.github.io/drat/', type='source')`
#Load in the data
world <- spData::world
wdb <- spData::worldbank_df
#Combine the data frames
wld_jn <- left_join(world, wdb, by = c('iso_a2', 'name_long' = 'name'))
#Reproject to required coordinate system. Obtained from: https://spatialreference.org/ref/sr-org/6/
wld_jn <- st_transform(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
def_wld_jn <- st_set_crs(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
#Graph mean urban population across continent:
wld_jn %>%
select(continent, urban_pop) %>%
drop_na() %>%
group_by(continent) %>%
mutate(mean_urban_pop = mean(urban_pop)) %>%
ggplot() +
geom_sf(aes(fill = mean_urban_pop, geometry = geom)) +
scale_fill_gradient2(midpoint = 285)+
guides(fill = guide_colorbar(title = "Population",
title.position = "bottom",
title.theme = element_text(size = 10,
face = "bold",
colour = "gray70",
angle = 0))) +
ggtitle("World Urban Population") +
theme(plot.title = element_text(hjust = 0.5))
由 reprex package (v2.0.1)
于 2022-02-23 创建
这就是您要找的吗？稍微采用了代码并且
看这里https://github.com/tidyverse/ggplot2/issues/3391
wld_jn %>%
select(continent, urban_pop) %>%
drop_na() %>%
group_by(continent) %>%
summarise(mean_urban_pop = mean(urban_pop)) %>%
ggplot(.) +
geom_sf(data = wld_jn, aes(fill = wld_jn$mean_urban_pop)) +
scale_fill_gradient2(midpoint = 285)+
guides(fill = guide_colorbar(title = "Population",
title.position = "bottom",
title.theme = element_text(size = 10,
face = "bold",
colour = "gray70",
angle = 0))) +
ggtitle("World Urban Population") +
theme(plot.title = element_text(hjust = 0.5))
我正在使用 R 中 spData 包中的 world 和 worldbank_df 数据集，我需要对我的数据进行子集化并绘制结果图表。我正在选择“大陆”和“urban_pop”列，删除所有 NA 值，按大陆分组，并汇总所有大陆的平均城市人口。但是，当我使用 geom_sf 调用绘制结果图表时，出现错误：
Error in FUN(X[[i]], ...) : object 'mean_urban_pop' not found
我需要在世界地图中绘制此数据，但它对我不起作用，因为 geom 坐标未传输到我的新数据集。
如何将这些结果绘制成图表？
注意：下面投影的坐标系是我必须使用的。
这是我的代码：
library(tidyverse)
library(sf)
library(spData)
library(ggplot2)
#Load in the data
world <- spData::world
wdb <- spData::worldbank_df
#Combine the data frames
wld_jn <- left_join(world, wdb, by = c('iso_a2', 'name_long' = 'name'))
#Reproject to required coordinate system. Obtained from: https://spatialreference.org/ref/sr-org/6/
wld_jn <- st_transform(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
def_wld_jn <- st_set_crs(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
#Graph mean urban population across continent:
wld_jn %>%
select(continent, urban_pop) %>%
drop_na() %>%
group_by(continent) %>%
summarise_at(vars(urban_pop), list(mean_urban_pop = mean)) %>%
ggplot(.) +
geom_sf(data = wld_jn, aes(fill = mean_urban_pop)) +
scale_fill_gradient2(midpoint = 285)+
guides(fill = guide_colorbar(title = "Population",
title.position = "bottom",
title.theme = element_text(size = 10,
face = "bold",
colour = "gray70",
angle = 0))) +
ggtitle("World Urban Population") +
theme(plot.title = element_text(hjust = 0.5))
看起来问题出在使用 summarise() 而不是 mutate() 上；当您使用 summarise() 时，您只保留感兴趣的变量，例如
library(tidyverse)
head(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
mtcars %>%
group_by(cyl) %>%
summarise(mean_mpg = mean(mpg)) %>%
head()
#> # A tibble: 3 × 2
#> cyl mean_mpg
#> <dbl> <dbl>
#> 1 4 26.7
#> 2 6 19.7
#> 3 8 15.1
mtcars %>%
group_by(cyl) %>%
mutate(mean_mpg = mean(mpg)) %>%
head()
#> # A tibble: 6 × 12
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb mean_mpg
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 19.7
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 19.7
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 26.7
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 19.7
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 15.1
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 19.7
由 reprex package (v2.0.1)
于 2022-02-23 创建如果您将 sumamrise() 更改为 mutate()（并将 geometry = geom 添加到 geom_sf()），则不会出现错误。这能解决您的问题吗？
library(tidyverse)
library(sf)
#> Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
#install.packages("spData")
library(spData)
#> To access larger datasets in this package, install the spDataLarge
#> package with: `install.packages('spDataLarge',
#> repos='https://nowosad.github.io/drat/', type='source')`
#Load in the data
world <- spData::world
wdb <- spData::worldbank_df
#Combine the data frames
wld_jn <- left_join(world, wdb, by = c('iso_a2', 'name_long' = 'name'))
#Reproject to required coordinate system. Obtained from: https://spatialreference.org/ref/sr-org/6/
wld_jn <- st_transform(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
def_wld_jn <- st_set_crs(wld_jn, '+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs')
#Graph mean urban population across continent:
wld_jn %>%
select(continent, urban_pop) %>%
drop_na() %>%
group_by(continent) %>%
mutate(mean_urban_pop = mean(urban_pop)) %>%
ggplot() +
geom_sf(aes(fill = mean_urban_pop, geometry = geom)) +
scale_fill_gradient2(midpoint = 285)+
guides(fill = guide_colorbar(title = "Population",
title.position = "bottom",
title.theme = element_text(size = 10,
face = "bold",
colour = "gray70",
angle = 0))) +
ggtitle("World Urban Population") +
theme(plot.title = element_text(hjust = 0.5))
由 reprex package (v2.0.1)
于 2022-02-23 创建这就是您要找的吗？稍微采用了代码并且 看这里https://github.com/tidyverse/ggplot2/issues/3391
wld_jn %>%
select(continent, urban_pop) %>%
drop_na() %>%
group_by(continent) %>%
summarise(mean_urban_pop = mean(urban_pop)) %>%
ggplot(.) +
geom_sf(data = wld_jn, aes(fill = wld_jn$mean_urban_pop)) +
scale_fill_gradient2(midpoint = 285)+
guides(fill = guide_colorbar(title = "Population",
title.position = "bottom",
title.theme = element_text(size = 10,
face = "bold",
colour = "gray70",
angle = 0))) +
ggtitle("World Urban Population") +
theme(plot.title = element_text(hjust = 0.5))